CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, and Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada.
CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA.
J Neurosci Methods. 2019 Jul 1;322:34-49. doi: 10.1016/j.jneumeth.2019.04.010. Epub 2019 Apr 23.
Simultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible.
We extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model. This allows a precise and reliable localization of epileptic foci in addition to predicting generator's behavior. The proposed method works in the source domain and delineates generators considering spatial correlation between spike template and candidate components in addition to patient's medical records.
The proposed method was applied on 20 patients with refractory epilepsy and 20 age- and gender-matched healthy controls. The identified components were examined statistically and threshold of localization accuracy was determined as 86% based on Receiver Operating Characteristic (ROC) curve analysis. Accuracy, sensitivity, and specificity were found to be 88%, 85%, and 95%, respectively. Contribution of EEG-fMRI and concordance between EEG and fMRI were also evaluated. Concordance was found in 19 patients and contribution in 17.
We compared the proposed method with conventional methods. Our comparisons showed superiority of the proposed method. In particular, when epileptogenic zone was located deep in the brain, the method outperformed existing methods.
This study contributes substantially to increasing the yield of EEG-fMRI and presents a realistic estimate of the neural behavior of epileptic generators, to the best of our knowledge, for the first time in the literature.
同步 EEG-fMRI 实验记录癫痫活动的时空动力学。基于尖峰的 EEG-fMRI 研究的一个缺点是,当没有尖峰可见时,它们无法提供关于癫痫发作源行为的信息。
我们从 EEG 上识别出癫痫成分的时间序列,并将其与广义线性模型 (GLM) 模型拟合。这除了能够预测发生器的行为外,还可以精确可靠地定位癫痫灶。该方法在源域中工作,并考虑到尖峰模板和候选成分之间的空间相关性以及患者的病历来描绘发生器。
该方法应用于 20 例难治性癫痫患者和 20 例年龄和性别匹配的健康对照者。对识别出的成分进行了统计学检验,并根据接收者操作特征 (ROC) 曲线分析确定了定位精度的阈值为 86%。发现准确性、敏感性和特异性分别为 88%、85%和 95%。还评估了 EEG-fMRI 的贡献和 EEG 与 fMRI 之间的一致性。在 19 名患者中发现了一致性,在 17 名患者中发现了贡献。
我们将提出的方法与传统方法进行了比较。我们的比较表明,该方法具有优越性。特别是当致痫区位于大脑深部时,该方法优于现有的方法。
本研究极大地提高了 EEG-fMRI 的产量,并首次在文献中提出了对癫痫发作源神经行为的现实估计。